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Apply by sending your CV or LinkedIn to [email protected] with the subject line: Senior Data Infrastructure Engineer.

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About Palabra

Palabra is on a mission to break down language barriers by delivering real-time speech-to-speech translation through our powerful SaaS. We empower developers to use our API and create transformative applications that redefine global communication.

About the Role

We’re building the backbone for a multi-petabyte audio-data platform—terabytes (1M+ hours) of raw speech - powered by unified, reliable pipelines and end-to-end governance. You’ll architect and operate our production data lifecycle: from raw-data ingestion to metadata/version control, lineage and provenance capture, and scalable delivery for downstream analytics and ML services. This is a broad remit - you’ll define standards, bring order to disparate pseudo-labeling flows, and enable multi-cloud flexibility based on cost and performance.

Key Responsibilities

1. Multi-Cloud Storage & Compute
   • Design storage strategies across GCS/S3 (or equivalents) and manage cost/performance trade-offs in at least two cloud providers.
   • Collaborate with CloudOps/Security to define IAM, secret management and compliance controls.
2. Infrastructure & Orchestration (DataOps)
   • Containerize data workflows (Docker + CI/CD) and integrate orchestration agents.
   • Embed autoscaling, health checks and cost-sensitive policies (spot instances) into data services.
3. Monitoring & Optimization
   • Implement observability for pipelines and storage, troubleshoot bottlenecks, and tune for latency, cost and reliability.
   • Define backup, disaster-recovery and data-safety strategies for critical stores.
4. Cross-Functional Collaboration
   • Partner closely with ML/DL engineers and DevOps/Cloud admins to align on requirements, workloads and deployments.
   • Produce clear system design docs, runbooks, and train stakeholders on new processes.
5. Data Governance & Lineage
   • Establish data versioning, metadata schemas, provenance tracking and auditability for raw audio and derived artifacts.
   • Own design and rollout of governance processes, policies and documentation (runbooks, onboarding guides).
6. Pipeline Architecture
   • Consolidate ad-hoc pseudo-labeling pipelines into a unified, fault-tolerant framework with queuing, partitioning, retry logic and observability.

Required Qualifications (5+ years)

• Data Engineering
  • 5+ years building and operating ETL/ELT pipelines in python, with queues, partitioning, retries and monitoring.
  • Hands-on with cloud object storage (GCS, S3 or equivalent) and strong grasp of metadata/versioning principles.
• Cloud & Containerization
  • Practical experience in at least one major cloud (GCP, AWS or Azure) and willingness to expand to multi-cloud.
  • Proficiency with Docker and CI/CD for data services; basic familiarity with Kubernetes or similar.
• Security & Reliability
  • Solid understanding of IAM, service accounts, secret management and secure credential handling.
  • Experience defining autoscaling policies, load-balancers and health-check integrations.
• Soft Skills
  • Excellent cross-functional communication and collaboration.
  • Documentation-minded: able to write clear runbooks, onboarding guides and architectural notes.